Description

This course aims to provide a comprehensive introduction to machine learning, with a primary focus on classification and regression approaches and the methodologies to apply them in practice. The main topics covered include:

- Data processing

- Supervised methods (decision tree, SVM, ...)

- Unsupervised methods (Kmeans, HAC, ...)

- Neural networks supervised and unsupervised

- Evaluation methods supervised (F1-score, accuracy, ...) and unsupervised (ARI, NMI, ...) 

Compétences requises

Write simple Python programs

Compétences visées

• Process the data to be used in a learning task

• Explain the principles of the main supervised and unsupervised methods

• Implement and use these methods

• Evaluate a learning result with the right tools, depending on the target objective

Disciplines

• Informatique

Bibliographie

• Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Contacts

Responsable(s) de l'enseignement

• Baptiste Lafabregue